How to facilitate understanding of data? Data visualization [Examples and tools]
Data is everywhere.
We live in a world where is a lot of data and we may even think that there is too much data.
It’s true.
There are a few signs that there’s too much data. There are more and more of them, it is difficult to analyze, process or draw conclusions from them, which is why it is so important to be able to present them in a proper way.
In 2000, when the telescope was launched in New Mexico, more data was collected than in the entire history of astronomy, and today more or less the same amount of data is collected every few weeks.
Walmart has 20,000 stores worldwide in 70 countries and processes 2.5 petabytes of customer data every hour. For comparison, 1.5 PT is 10 billion Facebook photos.
This is an unprecedented amount of data in the past.
It seems that we live in a world ruled by the need to collect data. This is what the economy of the modern world is based on. Each of us “produces” data, each of us has a phone in his pocket, where applications are installed, which send further information about the user. Actually, the data has become a new currency. How many times have you met with the situation that you got something “for free”, only for providing your data? Promotions to set up a bank account, permission to receive marketing materials such as e-books, applications — Facebook, search engine and Google tools.
Data is a new business.
From such a huge amount of information, the art is to select the most important data, invest time and human resources and do it in such a way as to interest the audience.
What’s the purpose of data visualization?
If you want your Facebook post to have record-breaking results — what do you do? You add catchy, attractive graphics. This works the same way with reports. Good visualization attracts attention, is easier to understand, and helps you reach your audience quickly. With dashboards and graphics adjust to target-group, even huge data can be clear and understandable. Why?
Because most people are visual. So if you want your meetings with colleagues to be effective and your customers to understand your data better and faster, you should turn boring charts into eye-catching graphics. Here are some interesting numbers that confirm the importance of visualization:
- People receive 90% of all information from their eyes,
- Photos increase the readability of the text by 80%,
- People remember 10% of what they hear, 20% of what they read and 80% of what they see,
- If the leaflet contains no illustrations, people will remember 70% of it. Adding graphics can increase the number to 95%.
Proper visualization of data also provides many benefits for your company:
- Quick decision making. Summarizing data is easy and fast thanks to graphics that allow you to quickly see if a specific column is higher than others if a given indicator exceeds a predetermined threshold, etc. — All this without the need to browse several pages of statistics in Google or Excel Sheets.
- More engagement. Most people are better at seeing and remembering information presented in graphics with clear messages.
- A better understanding of data. Well done reports are transparent not only to technical specialists, analysts and scientists dealing with data but also to non-technical managers such as CMO or CEO and help each employee make decisions in their area of responsibility.
With this influx of data, visual communication can be helpful and become a key aspect to attract and retain users for longer, or help stakeholders to understand and learn from the data presented — and this problem will be discussed in this article.
So, how do you do it?
The biggest problem in visualization of data is not, surprisingly, the selection of the wrong tool, lack of skills, but more prosaic thing of a strategic nature — the lack of orientation on the end-user, results in the fact that visualizations are often done automatically, without thinking whether the recipient, for whom the graphic representation is made — will understand the presented results and whether, above all, it will not take him more time than if the data were in the status quo.
This often results in a “mesh” of data when we want to present more data than the human brain can quickly and effectively analyze. Examples of how to NOT do visualization:
In order not to make such mistakes, I suggest you stick to the following (often forgotten) basic questions to make a proper mindset for your next data visualization challenge.
For whom do you direct this visualization? RECIPIENT
- Identify the highest priority people (e.g. teacher, classroom/management board, end-users), What are the current problems of the company, what are the expectations of the management and what are the difficulties that prevent the problem from being solved? Resist the temptation to create a visualization that meets the needs of each individual.
Why do you do this visualization? CONTEXT
- Specify what issues you would like to discuss in a presentation, e.g. a business presentation. It is worth considering the type of decision: strategic (e.g. just give one answer — whether to buy a given property) operational (issues requiring a response many times a day) or more tactical (issues requiring a regular weekly or monthly review at a meeting)
What do you want to achieve with the presentation?
- By a small selection of relevant information, you can significantly influence the next decisions of the stakeholders, e.g. by contrasting the sales results exceeding the statistically significant norm in a specific period of time.
How will you create this visualization? TYPE
- Standard charts or those that require more work, but bring better results (properly made — they affect emotions subconsciously, and then — going further — decisions) — artistic visualizations.
How to say if the visualization is “good”?
Three simple criteria. Good visualization should have:
Story (functional and at the same time engaging [storytelling] content)
Adjusted form (Design adapted to the target person e.g. weekly report — simple charts/presentation aimed at evoking emotions — artistic charts)
Values (consistency of information showing solution)
And what is “bad” visualization?
Bad visualization means:
Lack of functionality (effect — uselessness)
Lack of appropriate form (effect — misunderstanding)
Inconsistency (possible consequence — potential manipulation)
Examples of well-made visualization (subjective)
Interactivity
A perfect example of a data visualization that combines all the necessary ingredients of an effective and engaging piece: it uses colour to easily distinguish trends; it allows the viewer to get a global sense of the data; it engages users by allowing them to interact with the piece, and it is surprisingly simple to understand in a single glance.
Story
This example tells the story of every known drone attack (obviously not controlled by AI…) and the victims in Pakistan. By sorting the information, the dramatic facts were presented in an easy to understand visual format.
Time-saving
This visualization shows 100 years of the evolution of rock in a single page. Not only does it simplify information for you, but also provides actual audio samples for each genre, from electronic blues to dark metal.
The context
The goal of this insightful interactive piece by Nikon is to give users a sense of the size of objects, both big and small, by using comparisons. Next to the Milky way, for example, a common object such as a ball or a car seem smaller than we ever imagined.
An ideal example for presentations e.g. of the board of directors. The aim is to create a chart that shows the average price per carat of a diamond over five years. What should be done to attract attention, increase the memorization of the information, and perhaps even affect further decisions such as entering a specific market? Add the context — “Diamonds WERE a girl’s best friend”.
Which visualization tools should I use?
It depends. Data visualization is a form of communication used in various fields, e.g. science, business, journalism etc.
Therefore, everything depends on the purpose of presenting data, the level of advancement of visualization and your experience with a particular program.
If you are just beginning with data visualization or simply lack an idea for a proper graph, then visit the website — https://datavizproject.com
To select the right tool, you need to specify an (often contradictory) objective:
- Analysis or presentation? — Do you want to research data (R, Python) or build visualizations for e.g. a client (D3.js, Illustrator) or maybe something in between (Tableau, Ggvis, Plotly)?
- Changes — Will you change your data while doing the visualization? In Illustrator you have to start building your chart from the beginning when you change/add value. In D3.js you can change the data in an external location and update the database by re-importing. In Plotly and Lyra — just import the database once and you can freely change it in the tool without losing a lot of valuable time.
- Basic or unusual chart types? You need standard “bar” or “line” charts (Excel, Highcharts); or maybe more original? (D3.js). If you don’t know how to code, then the solution to the second situation may be the Lyra application, where you can change any element without entering even a single line of code.
- Interactivity vs. static: You need to create interactive graphics e.g. for a website (D3.js, Highcharts) or maybe you just need static graphics in PDF/SVG/PNG format (R, Illustrator).
“There are no perfect tools for everything, there are only good tools for people with specific goals.”
The tools should be tailored to a specific need (blue — software libraries, red — programs).
What tools do we use in Wrocode to visualize data?
Free
Google Fusion Tables (a great tool for presenting geographical data, unfortunately, Google is cancelling its support for this program on December 3, 2019), Tableau Public (easy to use — often used by us to present e.g. sales data)
Paid
Sisense (simple interface — huge possibilities → definitely worth recommending)
…and many others depending on need.
Do you have a small/medium enterprise and are interested in how new technologies such as Big Data can increase your competitiveness on the market? Maybe the online shop is such a company — if so, we recently wrote about Big Data solutions in eCommerce.
Or would you like to know more about the 10 most popular Big Data applications?
